Scaling clustering algorithm for data with categorical attributes

Authors:
Erendira Rendón Lara;R. Barandela
Affiliations:
Computer Systems Department, Technologic Institute of Toluca, México;Computer Systems Department, Technologic Institute of Toluca, México
Venue:
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Year:
2005

Citing 3
Cited 0

BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ROCK: A Robust Clustering Algorithm for Categorical Attributes

ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clustering constitutes an important task inside the fields of Pattern Recognition and Data Mining. Clustering of categorical data is a difficult problem and has not received the attention its importance deserves. In the present paper, we introduce a new clustering method to work with categorical data. The algorithm is easily scalable and yields better clustering results that the well-known K-MODES and Rock algorithms.